| Description |
1 online resource (xxvi, 607 pages) : illustrations |
| Series |
Series in machine perception and artificial intelligence ; v. 64 |
|
Series in machine perception and artificial intelligence ; v. 64.
|
| Contents |
Preface; Notation and basic terminology; Abbreviations; Contents; 1. Introduction; 1.1 Recognizing the pattern; 1.2 Dissimilarities for representation; 1.3 Learning from examples; 1.4 Motivation of the use of dissimilarity representations; 1.5 Relation to kernels; 1.6 Outline of the book; 1.7 In summary; PART 1 Concepts and theory; 2. Spaces; 3. Characterization of dissimilarities; 4. Learning approaches; 5. Dissimilarity measures; PART 2 Practice; 6. Visualization; 7. Further data exploration; 8. One-class classifiers; 9. Classification; 10. Combining |
| Summary |
This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition. Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and in |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Pattern perception.
|
|
Pattern recognition systems.
|
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Pattern Recognition, Automated
|
|
COMPUTERS -- Optical Data Processing.
|
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Pattern perception
|
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Pattern recognition systems
|
| Form |
Electronic book
|
| Author |
Duin, Robert P. W
|
| ISBN |
9812703179 |
|
9789812703170 |
|
9789812565303 |
|
9812565302 |
|
